ICLR 2020

687 papers

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Amartya Sanyal, Philip H. Torr, Puneet K. Dokania

Measuring the Reliability of Reinforcement Learning Algorithms

Stephanie C.Y. Chan, Samuel Fishman, Anoop Korattikara, John Canny, Sergio Guadarrama

Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

Byeongchang Kim, Jaewoo Ahn, Gunhee Kim

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

Masked Based Unsupervised Content Transfer

Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwang Hee Lee

Inductive and Unsupervised Representation Learning on Graph Structured Objects

Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni

Rotation-invariant clustering of neuronal responses in primary visual cortex

Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker

Batch-shaping for learning conditional channel gated networks

Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling

Learning Robust Representations via Multi-View Information Bottleneck

Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata

Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi

Deep probabilistic subsampling for task-adaptive compressed sensing

Iris A.M. Huijben, Bastiaan S. Veeling, Ruud J.G. van Sloun

Robust anomaly detection and backdoor attack detection via differential privacy

Min Du, Ruoxi Jia, Dawn Song

Self-labelling via simultaneous clustering and representation learning

Asano YM., Rupprecht C., Vedaldi A.

The intriguing role of module criticality in the generalization of deep networks

Niladri Chatterji, Behnam Neyshabur, Hanie Sedghi

Learning to Guide Random Search

Ozan Sener, Vladlen Koltun

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang

Lagrangian Fluid Simulation with Continuous Convolutions

Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, Vladlen Koltun

Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin

Differentiation of Blackbox Combinatorial Solvers

Marin Vlastelica Pogančić, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolinek

Compressive Transformers for Long-Range Sequence Modelling

Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap

Scaling Autoregressive Video Models

Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit

Causal Discovery with Reinforcement Learning

Shengyu Zhu, Ignavier Ng, Zhitang Chen

The Ingredients of Real World Robotic Reinforcement Learning

Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen

Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems

Chris Reinke, Mayalen Etcheverry, Pierre-yves Oudeyer

A Stochastic Derivative Free Optimization Method with Momentum

Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtarik

Restricting the Flow: Information Bottlenecks for Attribution

Karl Schulz, Leon Sixt, Federico Tombari, Tim Landgraf

Understanding and Improving Information Transfer in Multi-Task Learning

Sen Wu, Hongyang R. Zhang, Christopher Re

Learning To Explore Using Active Neural SLAM

Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov

EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks

Sanchari Sen, Balaraman Ravindran, Anand Raghunathan

Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel

Xin Qiu, Elliot Meyerson, Risto Miikkulainen

B-Spline CNNs on Lie groups

Erik J Bekkers

Neural Outlier Rejection for Self-Supervised Keypoint Learning

Jiexiong Tang, Hanme Kim, Vitor Guizilini, Sudeep Pillai, Rares Ambrus

Reducing Transformer Depth on Demand with Structured Dropout

Angela Fan, Edouard Grave, Armand Joulin

Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Karthikeyan K, Zihan Wang, Stephen Mayhew, Dan Roth

SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition

Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, weihao sun, Gautam Singh

RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

Roberta Raileanu, Tim Rocktäschel

Low-dimensional statistical manifold embedding of directed graphs

Thorben Funke, Tian Guo, Alen Lancic, Nino Antulov-Fantulin

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li

GraphSAINT: Graph Sampling Based Inductive Learning Method

Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner

You Only Train Once: Loss-Conditional Training of Deep Networks

Alexey Dosovitskiy, Josip Djolonga

Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

Dexter R.R. Scobee, S. Shankar Sastry

Projection-Based Constrained Policy Optimization

Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge

Infinite-Horizon Differentiable Model Predictive Control

Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutnik, Mark Cannon

Combining Q-Learning and Search with Amortized Value Estimates

Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber

Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators

Daniel Stoller, Sebastian Ewert, Simon Dixon

Decentralized Deep Learning with Arbitrary Communication Compression

Anastasia Koloskova*, Tao Lin*, Sebastian U Stich, Martin Jaggi

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control

Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham*, Jonathan Uesato*, Kai Xiao*, Sven Gowal*

Spectral Embedding of Regularized Block Models

Nathan de Lara, Thomas Bonald

Gradient ℓ_1 Regularization for Quantization Robustness

Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort

SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes

Johannes C. Thiele, Olivier Bichler, Antoine Dupret

On the Relationship between Self-Attention and Convolutional Layers

Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models

Xisen Jin, zhongyu wei, Junyi Du, Xiangyang Xue, Xiang Ren

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

Learning-Augmented Data Stream Algorithms

Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff

Structured Object-Aware Physics Prediction for Video Modeling and Planning

Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

What Can Neural Networks Reason About?

Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-Ichi Kawarabayashi

Incorporating BERT into Neural Machine Translation

Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin

Building Deep Equivariant Capsule Networks

Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma

MMA Training: Direct Input Space Margin Maximization through Adversarial Training

Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang

Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, Hongyuan Zha

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

Alexei Baevski, Steffen Schneider, Michael Auli

Meta-learning curiosity algorithms

Ferran Alet*, Martin F. Schneider*, Tomás Lozano-Pérez, Leslie Pack Kaelbling

Training individually fair ML models with sensitive subspace robustness

Mikhail Yurochkin, Amanda Bower, Yuekai Sun

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

Caglar Gulcehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman

A Generalized Training Approach for Multiagent Learning

Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal

Lookahead: A Far-sighted Alternative of Magnitude-based Pruning

Sejun Park*, Jaeho Lee*, Sangwoo Mo, Jinwoo Shin

Spike-based causal inference for weight alignment

Jordan Guerguiev, Konrad Kording, Blake Richards

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi SHEN, Guillaume Obozinski

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning

Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert

Understanding the Limitations of Conditional Generative Models

Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel

Learning from Rules Generalizing Labeled Exemplars

Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi

Demystifying Inter-Class Disentanglement

Aviv Gabbay, Yedid Hoshen

Mixed-curvature Variational Autoencoders

Ondrej Skopek, Octavian-Eugen Ganea, Gary Becigneul

BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations

Hyungjun Kim, Kyungsu Kim, Jinseok Kim, Jae-Joon Kim

Model-based reinforcement learning for biological sequence design

Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy

Directional Message Passing for Molecular Graphs

Johannes Gasteiger, Janek Groß, Stephan Günnemann

BayesOpt Adversarial Attack

Binxin Ru, Adam Cobb, Arno Blaas, Yarin Gal

Explanation by Progressive Exaggeration

Sumedha Singla, Brian Pollack, Junxiang Chen, Kayhan Batmanghelich

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee

Hypermodels for Exploration

Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen

RaPP: Novelty Detection with Reconstruction along Projection Pathway

Ki Hyun Kim, Sangwoo Shim, Yongsub Lim, Jongseob Jeon, Jeongwoo Choi

Dynamics-Aware Embeddings

William Whitney, Rajat Agarwal, Kyunghyun Cho, Abhinav Gupta

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network

Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura

Functional Regularisation for Continual Learning with Gaussian Processes

Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?

Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry

High Fidelity Speech Synthesis with Adversarial Networks

Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen

You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

Lasse Espeholt, Raphael Marinier, Piotr Stańczyk, Ke Wang, Marcin Michalski‎

AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing

Xingzhe He, Helen Lu Cao, Bo Zhu

Never Give Up: Learning Directed Exploration Strategies

Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot

Meta-Learning with Warped Gradient Descent

Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin

Convolutional Conditional Neural Processes

Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois

Fair Resource Allocation in Federated Learning

Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith

Smooth markets: A basic mechanism for organizing gradient-based learners

David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

Wei Wang, Bin Bi, Ming Yan, Chen Wu, Jiangnan Xia

Training binary neural networks with real-to-binary convolutions

Brais Martinez, Jing Yang, Adrian Bulat, Georgios Tzimiropoulos

Permutation Equivariant Models for Compositional Generalization in Language

Jonathan Gordon, David Lopez-paz, Marco Baroni, Diane Bouchacourt